{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "file = \"600519.csv\"\n",
    "dataframe = pd.read_csv(file, encoding=\"utf-8\", index_col=0)\n",
    "# 第一步：逐行计算累积值\n",
    "cum_sum = []\n",
    "_last_cum = 0\n",
    "for index, value in dataframe.iterrows():\n",
    "    tmp = value[\"TURNOVER\"] + _last_cum\n",
    "    cum_sum.append(tmp)\n",
    "    _last_cum = tmp\n",
    "# 新增一列，并用上一步的结果赋值\n",
    "dataframe[\"Turnover_CUMSUM\"] = cum_sum\n",
    "# 第三步：可视化效果\n",
    "dataframe[\"Turnover_CUMSUM\"].plot()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ac59ebe37160ed0dfa835113d9b8498d9f09ceb179beaac4002f036b9467c963"
  },
  "kernelspec": {
   "display_name": "Python 3.9.0 64-bit",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
